Image-Text Out-Of-Context Detection Using Synthetic Multimodal Misinformation

Fatma Shalabi, Huy H. Nguyen, Hichem Felouat, Ching-Chun Chang, Isao Echizen
1/29/2024
cs.CVcs.AIcs.CL

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Abstract

Misinformation has become a major challenge in the era of increasing digital information, requiring the development of effective detection methods. We have investigated a novel approach to Out-Of-Context detection (OOCD) that uses synthetic data generation. We created a dataset specifically designed for OOCD and developed an efficient detector for accurate classification. Our experimental findings validate the use of synthetic data generation and demonstrate its efficacy in addressing the data limitations associated with OOCD. The dataset and detector should serve as valuable resources for future research and the development of robust misinformation detection systems.

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Cite this paper

@article{shalabi2024imagetext,
  title  = {Image-Text Out-Of-Context Detection Using Synthetic Multimodal Misinformation},
  author = {Fatma Shalabi and Huy H. Nguyen and Hichem Felouat and Ching-Chun Chang and Isao Echizen},
  year   = {2024},
  eprint = {2403.08783},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.08783v1}
}

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